A review on massive MIMO Antennas for 5G communication systems on challenges and limitations

High data rate transfers, high-definition streaming, high-speed internet, and the expanding of the infrastructure such as the ultra-broadband communication systems in wireless communication have become a demand to be considered in improving quality of service and increase the capacity supporting gigabytes bitrate. Massive Multiple-Input Multiple-Output (MIMO) systems technology is evolving from MIMO systems and becoming a high demand for fifth-generation (5G) communication systems and keep expanding further. In the near future, massive MIMO systems could be the main wireless systems of communications technology and can be considered as a key technology to the system in daily lives. The arrangement of the huge number of antenna elements at the base station (BS) for uplink and downlink to support the MIMO systems in increasing its capacity is called a Massive MIMO system, which refers to the vast provisioning of antenna elements at base stations over the number of the single antenna of user equipment. Massive MIMO depends on spatial multiplexing and diversity gain in serving users with simple processing signal of uplink and downlink at the BS. There are challenges in massive MIMO system even though it contains numerous number of antennas, such as channel estimation need to be accurate, precoding at the BS, and signal detection which is related to the first two items. On the other hand, in supporting wideband cellular communication systems and enabling low latency communications and multi-gigabit data rates, the Millimeter-wave (mmWave) technology has been utilized. Also, it is widely influenced the potential of the fifth-generation (5G) New Radio (NR) standard. This study was specifically review and compare on a few designs and methodologies on massive MIMO antenna communication systems. There are three limitations of those antennas were identified to be used for future improvement and to be proposed in designing the massive MIMO antenna systems. A few suggestions to improve the weaknesses and to overcome the challenges have been proposed for future consideration

A review on massive MIMO antennas for 5G communication systems on challenges and limitations

High data rate transfers, high-definition streaming, high-speed internet, and the expanding of the infrastructure such as the ultra-broadband communication systems in wireless communication have become a demand to be considered in improving quality of service and increase the capacity supporting gigabytes bitrate. Massive Multiple-Input MultipleOutput (MIMO) systems technology is evolving from MIMO systems and becoming a high demand for fifth-generation (5G) communication systems and keep expanding further. In the near future, massive MIMO systems could be the main wireless systems of communications technology and can be considered as a key technology to the system in daily lives. The arrangement of the huge number of antenna elements at the base station (BS) for uplink and downlink to support the MIMO systems in increasing its capacity is called a Massive MIMO system, which refers to the vast provisioning of antenna elements at base stations over the number of the single antenna of user equipment. Massive MIMO depends on spatial multiplexing and diversity gain in serving users with simple processing signal of uplink and downlink at the BS. There are challenges in massive MIMO system even though it contains numerous number of antennas, such as channel estimation need to be accurate, precoding at the BS, and signal detection which is related to the first two items. On the other hand, in supporting wideband cellular communication systems and enabling low latency communications and multigigabit data rates, the Millimeter-wave (mmWave) technology has been utilized. Also, it is widely influenced the potential of the fifth-generation (5G) New Radio (NR) standard. This study was specifically review and compare on a few designs and methodologies on massive MIMO antenna communication systems. There are three limitations of those antennas were identified to be used for future improvement and to be proposed in designing the massive MIMO antenna systems. A few suggestions to improve the weaknesses and to overcome the challenges have been proposed for future considerations

Data-Rate Driven Transmission Strategies for Deep Learning Based Communication Systems

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions

Deep learning based pilot assignment in massive MIMO systems

Abstract. This thesis proposes a solution to the pilot contamination problem in massive multiple-input multiple-output systems by intelligently reusing pilot sequences using deep learning. The considered single-cell network is a massive machine-type communication system that has multiple sectors, each equipped with a uniform linear array antenna. Channels between the base station and the user equipment are modeled as spatially correlated and directive, where the angular domain interference primarily dictates pilot contamination. The main idea behind the proposed solution is that pilot sequences can be shared by a set of user equipment that do not have overlapping angle-of-arrival ranges at the base station, without causing significant mutual interference. The problem is formulated as a regression problem where the loss function represents the total pilot contamination in the network. A deep feedforward neural network architecture is used with the unsupervised learning approach to solve the problem, where the channel covariance matrices estimated at the base station are used as the input. A tailored training approach is proposed that is made up of two strategies as follows. First, the neural network is trained with constrained user equipment locations where the constraint gradually changes as the learning progresses. Second, the input data is rearranged to make the feature extraction easier for the neural network. Numerical experiments show that the proposed solution performs close to the exhaustive search solution when trained on a single network instance. When trained on a batch of training samples and validated on a batch of previously unseen samples, the proposed method generalizes well and subsequently performs on par with existing solutions.Syväoppimiseen pohjautuva pilottien allokointi massiivisissa moniantennijärjestelmissä. Tiivistelmä. Tässä opinnäytetyössä ehdotetaan ratkaisua pilottisekvenssien keskinäisen häiriön vaimentamiseksi massiivisissa moniantennijärjestelmissä pilottisekvenssien älykkäällä uudelleenkäytöllä syväoppimisen avulla. Tarkasteltu yksisoluinen verkko on massiivinen konetietoliikennejärjestelmä, jakaantuen useaan sektoriin, joista kukin toimii lineaarisella ryhmäantennilla. Tukiaseman ja käyttäjälaitteiden väliset kanavat ovat korreloituneita tilatasossa sekä suuntavia, joissa kulmatason häiriö on ensisijainen pilottihäiriön lähde. Ehdotetun ratkaisun pääajatus on, että pilottisekvenssit voidaan jakaa sellaisten käyttäjälaitteiden kanssa, joilla ei ole päällekkäisiä saapumiskulma-alueita tukiasemalla, täten aiheuttamatta merkittäviä keskinäisiä häiriöitä. Ongelma muotoillaan regressio-ongelmaksi, jossa kustannusfunktio edustaa verkon pilottihäiriön kokonaismäärää. Ongelman ratkaisemiseksi käytetään syvää eteenpäin kytkettyä neuroverkkoarkkitehtuuria ohjaamattoman oppimisen kanssa, jossa tulona käytetään tukiasemassa arvioituja kanavakovarianssimatriiseja. Työssä ehdotetaan kahta räätälöityä oppimisstrategiaa. Ensin neuroverkkoa koulutetaan rajoitetuilla käyttäjälaitteiden sijainneilla, joissa rajoitus muuttuu vähitellen oppimisen edetessä. Toiseksi syöttödata järjestetään uudelleen, jotta piirteiden erottaminen neuroverkolle olisi helpompaa. Numeeriset kokeet osoittavat, että ratkaisu on lähes optimaalinen, kun se koulutetaan yhteen verkkorealisaatioon. Kun ehdotettu menetelmä koulutetaan käyttäen harjoitusnäytteitä, ehdotettu menetelmä yleistyy hyvin uusiin näytteisiin sekä antaa yhtä hyvän suorituskyvyn kuin olemassa olevat ratkaisut

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction